1

Enhanced Pool-Boiling Heat Transfer and Critical Heat Flux on Femtosecond Laser Processed

Stainless Steel Surfaces

Corey M. Kruse

1, Troy Anderson

2, Chris Wilson

2, Craig Zuhlke

2, Dennis Alexander

2, George Gogos

1, Sidy Ndao*

1

University of Nebraska - Lincoln, 1Mechanical and Materials Engineering

2Electrical Engineering

Lincoln, Nebraska, U.S., 68588

*Email: sndao2@unl.edu

ABSTRACT

In this paper, we present an experimental investigation of pool boiling heat transfer on multiscale

(micro/nano) functionalized metallic surfaces. Heat transfer enhancement in metallic surfaces is very

important for large scale high heat flux applications like in the nuclear power industry. The multiscale

structures were fabricated via a femtosecond laser surface process (FLSP) technique, which forms self-

organized mound-like microstructures covered by layers of nanoparticles. Using a pool boiling

experimental setup with deionized water as the working fluid, both the heat transfer coefficients and

critical heat flux were investigated. A polished reference sample was found to have a critical heat flux of

91 W/cm2 at 40 °C of superheat and a maximum heat transfer coefficient of 23,000 W/m

2-K. The

processed samples were found to have a maximum critical heat flux of 142 W/cm2 at 29 °C and a

maximum heat transfer coefficient of 67,400 W/m2-K. It was found that the enhancement of the critical

heat flux was directly related to the wetting and wicking ability of the surface which acts to replenish the

evaporating liquid and delay critical heat flux. The heat transfer coefficients were also found to increase

when the surface area ratio was increased as well as the microstructure peak-to-valley height. Enhanced

nucleate boiling is the main heat transfer mechanism, and is attributed to an increase in surface area and

nucleation site density.

KEY WORDS: Pool Boiling, Critical Heat Flux, Femtosecond Laser Surface Processing, Heat Transfer

Coefficients, Metallic Enhanced Heat Transfer Surfaces

2

INTRODUCTION

Two-phase heat transfer on micro and nanostructured surfaces has attracted much research interest in

recent time [1]. This is due to the observed high heat transfer coefficients and critical heat flux. Most pool

boiling experiments for enhanced heat transfer have been conducted on micro and nanostructured surfaces

fabricated using complex fabrication techniques such as etching and thin film depositions carried out in

highly controlled environments (i.e., cleanroom). These techniques have been very effective at increasing

the critical heat flux (CHF) as well as heat transfer coefficients (HTC) through a combination of increased

surface roughness, wettability, and porosity. These microfabrication techniques have been used to create

very organized arrays of microstructures ranging from pillars to microchannels and have demonstrated

CHF values of 100 – 200 W/cm2 [2]–[4] for pillars and 100 W/cm

2 [5] for microchannels. When nanoscale

features are added to these microstructures, the critical heat flux can be further increased. Values as high as

230 W/cm2 [2] have been reported, showing that hierarchical structures can significantly increase the

performance of heat transfer surfaces. In addition to surface microstructuring, it has also been shown that

varying surface chemistry, such as provided by a combination or network of hydrophilic and hydrophobic

areas on a surface can significantly enhance the heat transfer performance as well. Hydrophobic surfaces

lead to easily activated nucleation sites, a decrease in the onset of nucleate boiling, and possible increase in

heat transfer coefficients, but result in a much lower critical heat flux [6]–[8]. Hydrophilic surfaces

meanwhile delay the formation of a stable vapor layer from forming and thus delaying the critical heat

flux. A combination of these types of surfaces allows for an optimization of the heat transfer performance

[6]–[9].

In addition to microstructures, silicon and copper nanowire coatings have also been used for two-phase

heat transfer enhancement. These types of surfaces have been shown to produce CHF values in the range of

120-250 W/cm2 [10]–[12]. Such CHF enhancements have been attributed to high nucleation site density,

superhydrophilicity, and enhanced capillary wicking.

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All of the previously mentioned experimental results were obtained with a heat transfer surface

comprised of a silicon base material which is advantageous in small applications such as electronics

cooling. These types of surface enhancement techniques are, however, not practical for applications which

require metallic heat transfer surfaces and much larger areas.

The enhancement of heat transfer using metallic surfaces is especially important for large scale

operations like in the nuclear power plant industry. Some work has been done to enhance heat transfer with

a metallic base surface using techniques such as anodizing processes or material deposition to achieve the

desired micro/nanostructures; however these techniques are often applicable to a limited type of base

material. For zircaloy-4 (commonly used in the nuclear industry), it has been shown that the critical heat

flux can be increased up to about 200 W/cm2 by using a simple anodizing method [13], [14]. This work

resulted in a very good enhancement of the critical heat flux but no real enhancement of the heat transfer

coefficients. In other studies, alumina was deposited onto platinum using atomic layer deposition [15] and

zinc oxide was deposited onto copper and aluminum surfaces in order to enhance two-phase heat transfer

[16]. The alumina deposition on platinum resulted in a CHF of around 120 W/cm2 while the zinc oxide

covered surface only reached maximum heat fluxes of about 80 W/cm2. Both techniques resulted in an

enhancement of the heat transfer coefficients. Anodizing has also been applied to aluminum surfaces [17]

to induce a nanoporous layer. This resulted in a CHF of around 90 W/cm2 but minimal heat transfer

coefficient enhancement. Other enhancement techniques have also been applied to metallic surfaces such

as copper. Surface sintering and the addition of nanorods have been shown to increase the heat transfer

coefficients of both types of surfaces but with no major enhancement of the critical heat flux [18], [19].

The present study focuses on the pool boiling heat transfer performance of 304 stainless steel multiscale

micro/nano-structures fabricated via Femtosecond Laser Surface Processing (FLSP). FLSP uses an ultra-

fast laser to ablate and form a self-organized array of mound like microstructures with a nanoparticle layer

on nearly any metallic surface [20]. This method is a one step process that has the advantage of being able

to process nearly any size of area with a very high precision.

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EXPERIMENTAL PROCEDURES

Surface Processing and Characterization

The impact of multiscale surface structures on the heat transfer coefficient and critical heat flux of

stainless steel was investigated through the characterization of four FLSP-generated samples with a

polished sample as a control. Structured surfaces fabricated via FLSP are characterized by a series of self-

organized quasi-periodic microstructure covered by a layer of nanoparticles[21]–[27]. Specific physical

characteristics including the peak to valley height, microstructure spacing, and nanoparticle layer thickness

are a function of the laser fluence and number of incident laser pulses. A schematic of the FLSP setup is

shown in Figure 1. The fabrication laser was a Ti:Sapphire (Spitfire, Spectra Physics) that produced ~50

femtosecond pulses with a central wavelength of 800 nm at a 1 kHz repetition rate. The laser power was

controlled through a combination of a half-wave plate and a polarizer. The pulses were focused using a

125 mm focal length plano-convex lens (PLCX-25.4-64.4-UV-670-1064) with a broadband antireflection

coating covering the laser spectrum. The sample was placed on a computer-controlled 3D translation stage

and translated through the beam path of the laser in order to process an area larger than the laser spot size.

The number of pulses incident on the sample was controlled by adjusting the translation speed of the

sample.

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Fig.1 Schematic of the femtosecond laser surface processing (FLSP) used in present study

The defining physical characteristics for the samples analyzed in the present study as well as the laser

parameters used in fabrication are summarized in Table 1; scanning electron microscope (SEM) images

and 3D profiles of the samples are shown in Figure. 2. The 3D profiles of the sample surfaces were

generated using a 3D confocal laser scanning microscope (Keyence VK-X200). The Peak to Valley

Height, RMS surface roughness, and Surface Area Ratio (total area of the microstructures divided by the

projected area) are measured with the Keyence instrument. The separation between the microstructures is

determined by a 2D Fast Fourier Transform analysis of the SEM image.

Table 1: Physical characteristics of the 304 stainless steel analyzed samples.

The control sample in the present study was 304 stainless steel polished to a mirror finish through the

use of a series of buffing compounds. As can be seen from Fig. 2, the FLSP samples have mound like

microstructures surrounded by deep valleys. Although it cannot be seen from the SEM images in Figure 2,

the mound like microstructures are covered in a layer of nanoparticles [23], [28]. Samples S1 – S3 are a

series created with varying laser fluence but constant shot number (N = 840). The surface structures are

Below Surface Growth (BSG) Mounds as described in Ref [21]. A visual inspection of Figure 2

demonstrates that the microscale structures in this series are similar in shape, but increase in size and

separation. Quantitative analysis shows that these samples all have roughly equivalent surface area ratios,

but have concurrently increasing peak-to-valley height, RMS surface roughness, and microstructure

separation. Sample S4 was fabricated with a higher laser fluence in order to generate Above Surface

Growth (ASG) Mounds [21]; this sample is characterized by taller, narrower microstructures surrounded

by circular pits.

Sample Fluence (J/cm2) Number of Shots Peak to Valley Height (µm) Surface Roughness (µm) Surface Area Ratio Separation (µm)

Polished -- -- -- 0.22 1.09 --

S1 0.7 840 7.1 1.4 3.85 6.0

S2 1.4 840 22.3 4.6 3.79 15.9

S3 2.1 840 31.3 7.8 3.82 26.1

S4 4.1 230 35.8 7.4 4.7 20.1

6

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Fig. 2 SEM and laser confocal microscope images of the femtosecond laser processed surfaces. Left –

Confocal microscope 3D profile, Right – SEM images

It is well known that the wetting and the wicking ability of a surface greatly affects its heat transfer

performance. The FLSP process has a significant impact on the wetting and wicking ability of the surface.

A Ramé -Hart Model 590 F4 Series Goniometer and Tensiometer was used to measure the contact angle of

both the polished and processed samples using deionized water. The contact angle of the polished surface

was found to be approximately 80° while FLSP surfaces all had contact angles of nearly zero. When a

small droplet is placed on each of the surfaces, the droplet is absorbed in its entirety into the surface in a

very fast manner. The surfaces are considered superwicking because the wetting front easily spreads across

the entire surface in a matter of a few seconds as confirmed by flow visualization using a high speed

camera. The superwicking behavior is attributed to the presence of the porous layer of nanoparticles that

induces high capillary wicking actions

Pool Boiling Experimental Setup and Procedure

A closed system pool boiling experimental setup was used for the heat transfer experiments. This setup

is highlighted in Figure 3. The experimental setup was designed to accommodate a variety of working

fluids. For the presented results, the test rig was filled with eight liters of deionized water. The system was

brought to the saturation temperature of the water at atmospheric pressure by the use of an immersion

heater (Omega - MT-112E2/120V) controlled by an analog variac. The water temperature and internal

pressure were monitored with the use of two K-type thermocouples (Omega - M12KIN-18-U-6-D) and a

pressure transducer (Omega - MMA100V5P4C0T4A5CE) connected to a National Instrument Data

Acquisition Board. The water was degassed for a half hour before measurements were taken; the

evaporated water was directed through a coil condenser (Quark Glass - qc-6-4sp) and noncondensable

gases were vented to the atmosphere. The condenser was supplied with cold water with the use of a chiller.

The experimental setup also includes a bladder accumulator for regulating the overall system pressure. This

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feature of the boiling rig was not used for the present experiment as all measurements were taken at

atmospheric pressure. Two Lexan view windows were incorporated into the test rig to allow for flow

visualization.

Once the system was allowed to reach the saturation temperature of the water, power to the heater was

gradually increased using a copper heating block containing five cartridge heaters (Omega - CSH-

203450/120V) controlled with an analog variac. This copper heating block was attached to the upper

copper heating block (see Fig. 3) with the use of a high temperature solder (McMaster - 7685A12). The

upper copper heating block has five thermocouple holes drilled to the center of the block. The

thermocouples (located 3.18 mm apart) were used to measure the temperature gradient within the block

and to calculate the heat flux. Heat flux values were recorded after the system had reached steady state

monitored through an in-house LabVIEW program. The test section consists of a 25.4 mm diameter and

.254 mm thick 304 stainless steel disk. The size of the test section was chosen to be sufficiently large in

order to eliminate any heater size effects on the critical heat flux. This was done by determining the Bond

number for the given heater size. This number gives a ratio of heater size to bubble departure size. A test

sample is considered to be sufficiently large if the Bond number is greater than 3 [29]. The Bond number is

calculated using Equation 1.

𝑩𝒐 =𝒈(𝝆𝒍−𝝆𝒗)

𝝈𝑫𝟐 (1)

Where g is the acceleration due to gravity, 𝝆𝒍 and 𝝆𝒗 are the liquid and vapor densities respectively, 𝝈 is

the surface tension of the liquid in air, and D is the diameter of the heater surface. Using this equation, the

Bond number was found to be around 100; therefore it can be assumed that the heater size can be

neglected. The stainless steel test section thickness was chosen to minimize the operating temperatures of

the heating block at high heat fluxes. The test section was brazed onto the copper heating block using a

silver solder paste (Muggyweld - SSQ-6) to ensure an efficient contact between the two. The surface

temperature of the test section was obtained with the use of the measured temperature gradient along the

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heating block. The contact resistance between the copper and stainless steel was neglected due to the very

thin and the highly conductive silver solder braze used. A high temperature PEEK plastic insulating

bushing was used to insulate the upper copper heating block. Fiberglass insulation (not shown in Fig. 3)

was used to insulate the lower copper heating block. High temperature silicon o-rings were used to seal

between the concentric heating and insulating pieces. To ensure that nucleation would not prematurely

occur on the outer edges of the boiling surface, a special epoxy (Mcmaster - 7513A1) was used for bonding

dissimilar materials.

Fig. 3 Left – Full experimental pool boiling setup, Right – Cross sectional view of heating block and

boiling surface

Heat Flux and Uncertainty Calculation

The five equally spaced thermocouples located in the upper heating block were used to measure the

temperature gradient along the axis of the heating block and calculate the heat flux. The heat flux was

calculated as:

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𝒒′′ = 𝒌𝒄𝑻𝒊−𝑻𝒋

𝒙 (2)

where 𝒌𝒄 is the thermal conductivity of the copper, 𝑻𝒊 and 𝑻𝒋 are the thermocouple temperatures and 𝒙 is

the thermocouple separation distance. The heat flux is calculated between alternating thermocouple

locations. To clarify, if the thermocouples are labeled in order from 1 to 5, the heat flux is calculated

between temperatures 1 and 3, 3 and 5, 2 and 4, and then 1 and 5. This increase in separation distance

reduces the uncertainty of the calculation. The minimum value of x is 6.35 mm. The thermal conductivity

of copper was taken to be constant at a value of 401 W/m-K. The temperature gradient and heat flux were

measured at the four locations and then averaged. The critical heat flux is determined when the monitored

thermocouple temperatures spike on the order of 100 °C and burnout occurs.

Radial heat losses were minimized by insulating the copper heating blocks. The upper heating block

was insulated with a PEEK plastic bushing (kp = .25 W/mK) with a thickness of .635 cm. The lower

heating block was insulated with fiberglass insulation. The thermocouple measurements were used to show

that the upper copper heating block was well insulated and at uniform temperature in the radial direction.

The radial heat loss can be estimated across the PEEK bushing if the aluminum housing (see Figure 3) is

assumed to be at 100 °C since it is in contact with the saturated water. At heat fluxes near the critical heat

flux, the maximum temperature in the upper copper heating block was measured to be 216 °C. It is

assumed that the copper/PEEK interface is at approximately 216 °C due to the highly conductive nature of

the upper copper heating block; hence the radial heat loss can be estimated by Equation 3.

𝒒′′𝒓 = 𝒌𝒑(𝑻𝒊−𝑻𝒘)

𝒓𝒑 (3)

Where 𝒌𝒑 is the PEEK thermal conductivity, 𝒓𝒑 is the PEEK thickness, 𝑻𝒘 is the aluminum temperature at

the wall, and 𝑻𝒊 is the interface temperature. It was found that this heat flux is approximately 0.5 W/cm2

which is less than 1% of the total heat flux measured at the critical heat flux. This approximation was also

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validated using a full 3D finite element analysis of the heating block, test surface, and insulation assembly.

The boundary conditions for the simulation are as follows: convective heat transfer coefficient of 23,000

W/m2-K at the boiling surface, heat flux of 92 W/cm

2 at the junction of the upper and lower copper heating

blocks, a constant temperature of 100 °C at the PEEK/water and aluminum housing/water interface, and a

natural convection coefficient of 10 W/m2-K at the lower part of the aluminum housing (see Figure 3). The

simulation results were in excellent agreement with the theoretical approximation obtained by Equation 3.

Uncertainties on the heat flux values as well as the surface temperatures were calculated using the

standard error propagation equation. The standard deviation of the heat flux based on the accuracies of the

measurement equipment is given as follows.

∆𝒒′′ = √(𝝏𝒒′′

𝝏𝒙∆𝒙)

𝟐

+ (𝝏𝒒′′

𝝏𝑻𝟏∆𝑻)

𝟐

+ (𝝏𝒒′′

𝝏𝑻𝟐∆𝑻)

𝟐

(4)

The variation of the thermocouple separation (∆𝒙 ) is .08 mm and the thermocouple variation (∆𝑻) is 1 °C.

This leads to an estimated error of approximately 7.5% at an average critical heat flux.

The surface temperature was calculated from the measured heat flux. This calculation considered both

the copper material as well as the stainless steel wafer. The contact between the two materials was assumed

to be ideal due to the highly conductive and very thin layer of silver braze used to join the two. The surface

temperature was therefore calculated as follows:

𝑻𝒔 = 𝑻𝟏 −𝒒′′𝒙𝟏

𝒌𝒄−

𝒒′′𝒙𝒔𝒔

𝒌𝒔𝒔 (5)

Where 𝑻𝟏 is the thermocouple temperature located closest to the surface, 𝒙𝟏 is the distance between 𝑻𝟏 and

the bottom of the stainless steel wafer, 𝒙𝒔𝒔 is the stainless steel wafer thickness, and 𝒌𝒄 and 𝒌𝒔𝒔 are the

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copper and stainless steel thermal conductivities respectively, allowing for variation with temperature. The

estimated error for the surface temperature can be determined using the following equation.

∆𝑻𝒔 = √(𝝏𝑻𝒔

𝝏𝑻𝟏∆𝑻)

𝟐

+ (𝝏𝑻𝒔

𝝏𝒒′′∆𝒒′′)

𝟐

+ (𝝏𝑻𝒔

𝝏𝒙𝟏∆𝒙𝟏)

𝟐

+ (𝝏𝑻𝒔

𝝏𝒙𝒔𝒔∆𝒙𝒔𝒔)

𝟐

(6)

∆𝒙𝟏, the variation of 𝒙𝟏, is 0.07 mm and ∆𝒙𝒔𝒔 the variation of the wafer thickness is 0.003 mm. With these

variations the error in the surface superheat temperature calculation at the critical heat flux is around 6 –

13% depending on the surface.

In a similar fashion, the uncertainty of the heat transfer coefficients can also be determined using the

extreme cases of the heat flux and surface temperature uncertainties. Using the standard error propagation

method, uncertainties of the heat transfer coefficient range from 11 – 16% depending on the test surfaces.

RESULTS AND DISCUSSION

For each of the sample surfaces tested, measurements were first taken at low heat fluxes and then the

heat flux was gradually increased until critical heat flux was reached. The results are shown in Figure 4. It

can be seen from this figure that there was a drastic difference between the processed and unprocessed

surfaces in their heat transfer performance. The processed surfaces consistently outperformed the polished

sample at every surface temperature. The polished sample was found to have a critical heat flux of 91

W/cm2 at a surface superheat of 40°C. This result closely matches the results found in the literature for a

similar smooth metallic surface [13], [30], [31], as well as critical heat flux correlations [32] such as

Zuber’s and Kandlikar’s. Zuber’s model is given in Equation 7 and Kandlikar’s model is given in Equation

8.

𝑞𝑐 = 𝐾ℎ𝑓𝑔𝜌𝑣 [𝜎𝑔(𝜌𝐿−𝜌𝑣)

𝜌𝑣2 ]

1

4 (7)

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𝑞𝑐 = ℎ𝑓𝑔𝜌𝑣.5(

1+cos 𝛽

16) [

2

𝜋+

𝜋

4(1 + 𝑐𝑜𝑠𝛽)]

1/2

𝑥[𝜎𝑔(𝜌𝐿 − 𝜌𝑣)]1/4 (8)

The value of the constant K is .131 as determined by Zuber. The terms ℎ𝑓𝑔, 𝜌𝑣, 𝜌𝐿, 𝜎, and g are the latent

heat of vaporization, the vapor density, the liquid density, the surface tension, and gravity. These properties

are evaluated for water at 100 °C. The β term in Equation 8 is the receding contact angle for the polished

stainless steel surface which was measured to be 70°. Zuber’s equation predicts a critical heat flux of about

110 W/cm2; this prediction overestimates the measured result for the polished sample in the present

experiments. This discrepancy can be attributed to the fact that Zuber’s model does not take into account

the surface wettability which we know has significant effects on the critical heat flux. Using Kandlikar’s

model, the critical heat flux was predicted to be 91.7 W/cm2 and agreed extremely well with our measured

value.

The reported critical heat flux of the polished sample agrees well with theory and other values found in

the literature, but there is a variation in the surface temperature reported. The polished sample in our work

was found to have a wall superheat temperature of 40 °C at the critical heat flux. In various published data

[13], [30] and [31] the wall superheat temperature has been shown to range from 10 °C to 55 °C. Even

though all the above referenced experiments had Bond numbers greater than 3, they significantly differ in

heater thickness. It has been reported that heater thickness and thermal conductivity can have an effect on

the heat transfer coefficient [33], [34]. The differences in wall superheat between the present experiments

and published literature could therefore be likely due to variances in heater thickness.

The maximum CHF observed was with sample S1 which had a CHF of 142 W/cm2. All processed

surfaces had consistently higher CHF values than the polished sample. An overview of the boiling

performance for each test sample is given in Table 2.

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Table 2. Summary of boiling performance for each test surface.

Although the four processed samples had the same contact angle, there is a significant difference

between the samples which can no longer be related to a change in the contact angle. It is well known that

reducing the contact angle to zero does not result in reaching a limit on critical heat flux enhancement and

that additional mechanisms become dominant. As can be seen from Table 2 and Figure 4, there is a definite

trend in CHF enhancement for samples S1-S3. For these surfaces the surface area ratio is nearly constant

but the critical heat flux increases with decreasing structure height and spacing. The reason for this increase

in CHF for these samples can be related to the microstructure geometry. As can be seen from Figure 2, the

area between the microstructures increases from S1 to S3. For S1 the mound structures are densely packed

and narrow channels or cracks are formed between microstructures. This network of channels, in addition

to the nanoparticle layer on the microstructures, allows for high capillary wicking effects which help to

quickly replenish the heated surface with cold liquid after local evaporation occurs and consequently delay

the critical heat flux. As the sample number increases, the separation and size of the microstructures also

increase; this results in the formation of deep pits in between the microstructures instead of the channels.

These deep pits and holes reduce the wicking potential of the surfaces and the wicking effect is mainly

dominated by the presence of the layer of nanoparticles covering the mound structures.

Polished 91 39.8 10.8 22,900

S1 142 29.2 8.6 48,600

S2 118 22.6 8.7 52,200

S3 109 22.8 8.2 47,800

S4 122 18.1 7 67,400

Sample

CHF

(W/cm2)

CHF Surface

Superheat (°C)

Onset of Nucleate

Boiling Superheat (°C)

Maximum Heat Transfer

Coefficient (W/m2-K)

15

Fig. 4 Heat fluxes with respect to surface superheat for both the laser processed and polished stainless steel

surfaces

As mentioned earlier, sample S4 is slightly different than the other samples. However, its critical heat

flux enhancement can still be explained by the same approach. S4 resulted in the second highest CHF of

122 W/cm2 observed, however had a larger peak-to-valley height as well as structure spacing. The most

notable geometric difference between S4 and the rest is the surface area ratio as its surface area ratio is

significantly higher than the other samples. This increase in the surface area ratio is the reason for the

increase in critical heat flux over sample S2 which had a much denser microstructure arrangement. The

larger surface area ratio results in more surface area in contact with the liquid and thus can compensate for

a lack of wicking ability due to the larger microstructure spacing. The increase in surface area ratio can

also result in a better wetting surface as described by the classic Wenzel model for a droplet on a rough

surface. The contact angle measurements taken were not able to prove a difference in wettability between

the processed surfaces due to the highly wicking nature of the processed surfaces and the limits of the

measuring device. Hence, S4 has higher CHF than S2 and S3 because of its relatively higher wettability. It

can be therefore concluded from the observed results, that the enhancement in CHF is due to a combination

of both surface wettability and capillary wicking.

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The local maximum superheat temperature that arises in S2-S4 can be related to the thermal

conductivity of the surface and the active nucleation site density. Since the thermal conductivity of

stainless steel is relatively low compared to other metals such as copper, local sites with different heat

fluxes can occur [33]. As the number of active nucleation sites increase the average surface temperature

would then decrease. Because the processed surfaces have roughness on both the nano and microscale,

there is a wide range of potential nucleation sites that could be activated at high heat fluxes. Near the

critical heat flux, the maximum amount of nucleation sites is active and thus reduces the surface

temperature. The polished sample does not have this nano and microscale roughness so there are no

additional nucleation sites to activate at high heat fluxes and thus the curve remains nearly linear.

Fig. 5 Heat transfer coefficient with respect to wall heat flux for each sample surface

The shift of the boiling curves to the left with the processed surfaces is very advantageous to heat

transfer applications as it corresponds to an enhancement of the heat transfer coefficients and allows for a

large amount of heat to be transferred at relatively low surface temperatures. The steep slope of the curve is

also advantageous because of the relatively small surface temperature change (around 10 °C for S4). For all

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processed surfaces investigated, significant enhancement of the heat transfer coefficients were observed in

comparison to the polished surface.

Heat transfer coefficients based on the projected area were determined for each of the test samples and

plotted in Fig. 5. A summary of the maximum heat transfer coefficients is also given in Table 2. The

maximum heat transfer coefficient values varied from 22,900 to 67,400 W/m2-K. As expected for nucleate

boiling, the heat transfer coefficients increase with increasing heat flux. The shape of each curve looks

nearly the same for heat flux values up to 90 W/cm2. Beyond this point the slope of the curve for the

processed samples changes significantly. This change in slope is a result of the additional nucleation sites

which become active at higher heat fluxes (e.g., smaller radius cavities); the probability of active

nucleation sites in this region is expected to be different for each test sample.

Samples S1-S3 have very similar heat transfer coefficient curves. This is expected since these surfaces

have nearly identical surface area ratios. The curves of S1-S3 diverge at around 90 W/cm2. Beyond this

point the heat transfer coefficients follow the trend of increasing microstructure peak-to-valley height. The

microstructures act like cooling fins for heat to be conducted through. As the height of the microstructures

increase, the more efficiently the surface is cooled and thus increases the heat transfer coefficient. Sample

S4 consistently has higher heat transfer coefficients than all the other samples. The overall enhancement of

the heat transfer coefficients can be attributed to efficient nucleate boiling and an increase in the surface

area ratio. The increase in the slope beyond the divergent point (at 90 W/cm2) can also be related to the

microstructure peak-to-valley height. As the height is increased the change in slope is also increased. The

enhancement of the heat transfer coefficients at high heat fluxes is due to a combination of the tall nature of

the microstructures [33] and the higher surface area ratio, as well as a higher probability of finding a

nucleation site which can be activated at higher heat fluxes.

As for the Onset of Nucleate Boiling (ONB), it can be seen from Figure 4 and Table 2 that the ONB

occurred at much lower wall superheat values for the processed surfaces when compared to the polished

surface. This is due to the nano and microscale features present on the processed surfaces which allow for

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nucleation sites that activate with less energy. Figure 6 shows the difference in nucleation between S4 and

the polished surface at low heat fluxes further supporting the above mentioned statement. As can be seen

from Figure 6, sample S4 has a much higher nucleation site density and produces much smaller bubbles

that quickly detach from the surface compared to the polished sample. This difference in bubble size and

departure rate and diameter also occurred at higher heat fluxes and surface temperatures. Sample S4 was

found to have the smallest ONB at around 7 °C compared to about 10 °C for the polished sample.

19

Fig. 6 Near Onset of Nucleate Boiling: Top – polished sample, 13 °C superheat and 3 W/cm2, Bottom – S4,

7.7 °C and 2 W/cm2

CONCLUSIONS

Through the use of femtosecond laser surface processing (FLSP), functionalized stainless steel surfaces

were fabricated with hierarchical micro/nanostructures. The structures consist of mound like

microstructures covered by a dense layer of nanoparticles resulting in highly wetting and superwicking

surfaces with augmented surface area and increased nucleation site density. Pool boiling heat transfer

experimental results revealed that using the laser processed stainless steel surfaces, the critical heat flux

can be increased from 91 to 142 W/cm2 while also increasing the maximum heat transfer coefficients from

23,000 to 67,400 W/m2-K. Increase in the critical heat flux has been attributed to increase in surface

wettability and wicking capabilities. Processed surfaces with tightly packed mound structures resulted in

better wettability and wicking and thus resulted in higher critical heat fluxes. The enhancement in heat

transfer coefficients are related to the surface area ratio, structure height, and the active nucleation site

density. It was found that the higher the surface area ratio the higher the heat transfer coefficients. It was

also found that microstructure peak-to-valley height can result in a cooling fin effect and further increase

the heat transfer coefficients at high heat fluxes. The heat transfer enhancement at low heat fluxes can be

attributed to the increase in nucleation site density and more efficient bubble departure dynamics.

ACKNOWLEDGMENT

This work has been supported by a grant through the Nebraska Center for Energy Sciences Research

(NCESR) with funds provided by Nebraska Public Power District (NPPD) to the University of Nebraska –

Lincoln (UNL) No. 4200000844, a NASA EPSCoR Grant # -NNX13AB17A and by funds from the

20

Department of Mechanical and Materials Engineering and the College of Engineering at UNL, awarded to

SN.

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